Corrosion rate prediction involves developing a predictive model that provides a realistic estimate, utilizing common operational parameters, existing lab/field data, and theoretical models. The novel Case-based Reasoning – Taylor Series (CBR-TS) model for corrosion prediction developed in this thesis, takes knowledge from existing field cases and uses CBR techniques and Taylor series to predict corrosion rates for new fields having similar parameters. The model predicts corrosion in three steps: case search (selection of similar cases), case ranking (by using Taylor series expansion), and case revision (by using correction factor from mechanistic or semi-empirical model). The model is implemented as a prototype and verified on a large hypothetical case base and a small field database with real data.